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Intraoperative Vascular Detection and Three-Dimensional Reconstruction using statistical Variance and Infrared Optical Tracking in High Frequency Ultrasound Imaging

Intraoperative Vascular Detection and Three-Dimensional Reco...

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conference on Clinical and Translational Neurophotonics

作者： Gupta, Shaurya Chen, Chaoliang Dobashi, Yuta Cherin, Emmanuel Ramjist, Joel Foster, Stuart Yee, Albert Yang, Victor X. D. Univ Toronto Inst Biomat & Biomed Engn Toronto ON Canada Ryerson Univ Dept Biomed Phys Toronto ON Canada Univ Toronto Inst Med Sci Sch Grad Studies Toronto ON Canada Sunnybrook Hlth Sci Ctr Sunnybrook Res Inst Toronto ON Canada Univ Toronto Div Orthoped Surg Dept Surg Toronto ON Canada Univ Toronto Dept Surg Div Neurosurg Toronto ON Canada

ISBN: (纸本)9781510623712

Intraoperative characterization of blood flow and visualization of microvasculature can have a huge impact on surgical outcomes. Knowledge about vasculature can provide diagnostic leverage, reducing operating times and improving patient recovery. Currently used Doppler-based techniques suffer from various shortcomings such as poor spatial resolution, high susceptibility to motion artifacts, and the inability to detect longitudinal flows. Our aim is to develop a fast, non-invasive approach to intraoperative microvascular imaging of slow-moving blood. In this work, we present a spatio-temporal approach to detect blood flow in vessels on the order of 0.1 mm. Specifically, a speckle-variance flow processing algorithm is used to detect small changes in B-mode pixel intensity on a micro-ultrasound (mu US) system operating in the range of 22-70 MHz. Data used in this study was acquired intraoperatively for patients undergoing neurosurgical procedures. Microcirculation was clearly visible in various anatomical structures and the spatial resolution in flow detection was much superior in comparison to Doppler-based flow detection. Moreover, using infrared optical tracking (Northern Digital Inc., Waterloo, Canada), a three-dimensional reconstruction of the microvasculature was constructed. This 3D vessel map allows for better visualization of the vasculature in the surgical cavity-allowing surgeons to plan their incisions, minimizing blood loss and potentially improving patient outcomes. To our knowledge, this is the first implementation of a three-dimensional, intraoperative microcirculation imaging technique using statistical and optical methods, alongside a non-Doppler high frequency ultrasound.

关键词： Blood flow detection Microvasculature High frequency ultrasound Doppler 3D image-guided surgery Speckle Variance

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image Super-Resolution via Deterministic-stochastic Synthesis and Local statistical Rectification

Image Super-Resolution via Deterministic-Stochastic Synthesi...

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11th ACM SIGGRAPH conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SA)

作者： Ge, Weifeng Gong, Bingchen Yu, Yizhou Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9781450360081

Single image superresolution has been a popular research topic in the last two decades and has recently received a new wave of interest due to deep neural networks. In this paper, we approach this problem from a different perspective. With respect to a downsampled low resolution image, we model a high resolution image as a combination of two components, a deterministic component and a stochastic component. The deterministic component can be recovered from the low-frequency signals in the downsampled image. The stochastic component, on the other hand, contains the signals that have little correlation with the low resolution image. We adopt two complementary methods for generating these two components. While generative adversarial networks are used for the stochastic component, deterministic component reconstruction is formulated as a regression problem solved using deep neural networks. Since the deterministic component exhibits clearer local orientations, we design novel loss functions tailored for such properties for training the deep regression network. These two methods are first applied to the entire input image to produce two distinct high-resolution images. Afterwards, these two images are fused together using another deep neural network that also performs local statistical rectification, which tries to make the local statistics of the fused image match the same local statistics of the groundtruth image. Quantitative results and a user study indicate that the proposed method outperforms existing state-of-the-art algorithms with a clear margin.

关键词： Deep Learning image Superresolution Deterministic Component stochastic Component Local Correlation Matrix Local Gram Matrix

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Research on yhe Approximation Properties of Two-Dimensional stochastic Bases in image Reconstruction Problems

Research on yhe Approximation Properties of Two-Dimensional ...

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International conference on Complex Systems: Control and Modeling Problems (CSCMP)

作者： Vitaliy Batishchev Aleksey Zolin Vladimir Yakimov Samara StateTechnical University Samara Russia

This paper discusses the problems of constructing a basis for two-dimensional stochastic functions, which are samples of random numbers with a given distribution law. The algorithm for constructing an approximation model with the use of this basis is described. The general algorithm for two-dimensional signal recovery using the stochastic basis by the least squares method with a given weight function is given. The approach to solving the problem of recovery of blurred images with a known point scattering function by constructing an inverse filter model is proposed. The approbation of the algorithms was performed on model examples and in the processing of real images obtained by remote sensing of the Earth. To quantify the quality of the recovery, we used a relative root mean square measurement of the difference between the reference and reconstructed images.

关键词： approximation theory image reconstruction mean square error methods stochastic processes Mean square error image reconstruction Approximation Theory Markov chain Earth remote sensing stochastic Processes Signal restoration stochastic random numbers WEIGHTING FUNCTIONS

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Improved image Quality Retinal Fundus with Contrast Limited Adaptive Histogram Equalization and Filter Variation

Improved Image Quality Retinal Fundus with Contrast Limited ...

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International conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Erwin Rido Zulfahmi Dea Sella Noviyanti Ghiena Rifka Utami Alya Nathasya Harison Putra Sunan Agung Jurusan Sistem Komputer Universitas Sriwijaya Indralaya Indonesia Universitas Pembangunan Nasional Veteran Jakarta Jakarta Indonesia

ISBN: (纸本)9781728129310

The use of retinal fundus images in the detection and initial diagnosis of abnormalities or diseases in the retina at this time has become one of the areas of interest to researchers, but these fundus images sometimes have low quality. Therefore, the quality of the fundus image must be improved first in order to facilitate the next process and obtain accurate results. In this study, we used the Contrast Limited Adaptive Histogram Equalization (CLAHE) method with filter order statistics, median filters, Gaussian filters, and Wiener filters using the STARE dataset to improve the quality of retinal images. In this paper, we use Mean Square Error (MSE) and Peak Signal Noise to Ratio (PSNR) as parameters for comparison between methods. From the several methods used, the lowest MSE value is 12.668 which is the result of the Order statistical Filter and Gaussian Noise method, while the highest PSNR value is 46.58359 dB which is the result of CLAHE and Median Filter.

关键词： biomedical optical imaging diseases eye Gaussian noise image filtering mean square error methods median filters medical image processing statistical analysis Wiener filters Mean square error median filters Wiener filters Biomedical optical imaging adaptive histogram equalization image filtering Medical image processing Gaussian noise maladjustment Eye statistical methods statistical analysis Filters

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Analysis of the Driver's Breaking Response in the Safety Cut-in Scenario Based on Naturalistic Driving

Analysis of the Driver's Breaking Response in the Safety Cut...

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SAE 2019 New Energy and Intelligent Connected Vehicle Technology conference

作者： Zhang, Jiarui Ma, Zhixiong Zhu, Xichan Tongji University China

For the personification of automotive vehicle function performance under common traffic scenarios, analysis of human driver behavior is necessary. Based on China Field Operational Test (China-FOT) database of China Natural Driving Study project, this paper studies the driver's response in the common cut-in scenario. A total of 266 cut-in cases are selected by manual interception of driving recorder video. The relevant traffic environment characteristics are also extracted from video, including light conditions, road conditions, scale and lateral position of cut-in vehicle, etc. Dynamic information is decoded form CAN, such as speed, acceleration and so on. Then image processing results, such as relative speed and distance of cut-in and subject vehicles, are calculated. statistical results based on above information show the response type and distribution of human driver: The behavior of keeping lane is 96.24%, in which the ratio of braking response is 51.13%. According to this, we choose to further research the behavior of keeping lane, and analyze the influencing factors of braking response. statistical methods, such as Chi-square test, Spearman correlation test, are used to verify the relativity between the factor introduced above and braking. And test results indicated traffic flow, cut-in vehicle type, relative speed and distance of two vehicles are significantly correlated with the driver's braking response. Finally, a logistic regression model of the braking response probability is established with the relevant parameter. The model shows the braking probability of human drivers, when faced with cut-in vehicle. © 2020 SAE International. All rights reserved.

关键词： statistical tests

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Deep Bayesian Mining, Learning and Understanding 19

Deep Bayesian Mining, Learning and Understanding

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25th ACM SIGKDD International conference on Knowledge Discovery & Data Mining (KDD)

作者： Chien, Jen-Tzung Natl Chiao Tung Univ Hsinchu Taiwan

ISBN: (纸本)9781450362016

This tutorial addresses the advances in deep Bayesian mining and learning for natural language with ubiquitous applications ranging from speech recognition to document summarization, text classification, text segmentation, information extraction, image caption generation, sentence generation, dialogue control, sentiment classification, recommendation system, question answering and machine translation, to name a few. Traditionally, "deep learning" is taken to be a learning process where the inference or optimization is based on the real-valued deterministic model. The "semantic structure" in words, sentences, entities, actions and documents drawn from a large vocabulary may not be well expressed or correctly optimized in mathematical logic or computer programs. The "distribution function" in discrete or continuous latent variable model for natural language may not be properly decomposed or estimated. This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process, Chinese restaurant process, hierarchical Pitman-Yor process, Indian buffet process, recurrent neural network (RNN), long short-term memory, sequence-to-sequence model, variational auto-encoder (VAE), generative adversarial network (GAN), attention mechanism, memory-augmented neural network, skip neural network, stochastic neural network, predictive state neural network, policy neural network. We present how these models are connected and why they work for a variety of applications on symbolic and complex patterns in natural language. The variational inference and sampling method are formulated to tackle the optimization for complicated models. The word and sentence embeddings, clustering and co-clustering are merged with linguistic and semantic constraints. A series of case studies are presented to tackle different issues in deep Bayesian mining, learning and understanding. At last,

关键词： deep learning Bayesian learning natural language processing

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ENHANCING PRODUCT imageS FOR CLICK-THROUGH RATE IMPROVEMENT 25

ENHANCING PRODUCT IMAGES FOR CLICK-THROUGH RATE IMPROVEMENT

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25th IEEE International conference on image processing (ICIP)

作者： Chae, Yeongnam Nakazawa, Mitsuru Stenger, Bjorn Rakuten Inc Rakuten Inst Technol Tokyo Japan

ISBN: (纸本)9781479970612

This paper proposes a statistical method to enhance image quality in order to increase the click-through rate (CTR) of product images. We build a joint probability model of global image features for photos of different product categories. The images are modified in terms of brightness, contrast, and sharpness in order to increase the expected CTR. The effectiveness of the method is evaluated using a perceptual user study, comparing it to histogram equalization methods, and by conducting an A/B test over a one-week period on the e-ommerce site Rakuten Ichiba.

关键词： image enhancement click-through rate advertisement e-commerce

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Steganalysis Based on Reducing the Differences of image statistical Characteristics 9

Steganalysis Based on Reducing the Differences of Image Stat...

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9th International conference on Graphic and image processing (ICGIP)

作者： Wang, Ran Niu, Shaozhang Ping, Xijian Zhang, Tao Beijing Univ Posts & Telecommun Beijing Peoples R China Zhengzhou Informat Sci & Technol Inst Zhengzhou Henan Peoples R China Zhengzhou Shengda Univ Econ Business & Management Zhengzhou Henan Peoples R China

ISBN: (数字)9781510617421

ISBN: (纸本)9781510617421

Compared with the process of embedding, the image contents make a more significant impact on the differences of image statistical characteristics. This makes the image steganalysis to be a classification problem with bigger within-class scatter distances and smaller between-class scatter distances. As a result, the steganalysis features will be inseparate caused by the differences of image statistical characteristics. In this paper, a new steganalysis framework which can reduce the differences of image statistical characteristics caused by various content and processing methods is proposed. The given images are segmented to several sub-images according to the texture complexity. Steganalysis features are separately extracted from each subset with the same or close texture complexity to build a classifier. The final steganalysis result is figured out through a weighted fusing process. The theoretical analysis and experimental results can demonstrate the validity of the framework.

关键词： Steganalysis image statistical characteristics image classification image segmentation

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基于无人机倾斜摄影的露头多点地质统计模拟——以山西吕梁坪头乡石盒子组为例

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石油学报 2021年第2期42卷 198-216页

作者：印森林高阳胡张明熊亭冯文杰赵俊威程乐利长江大学录井技术与工程研究院湖北荆州434023 长江大学地球科学学院湖北武汉430100 中国石油新疆油田公司勘探开发研究院新疆克拉玛依834000 中国石油天然气西部钻探工程公司新疆克拉玛依834000

针对传统野外露头研究方法的不足,利用无人机倾斜摄影具有敏捷性、视角全局性及定量化程度高的特点,对野外三角洲露头数据进行了采集、处理与地质解释,建立了三维数字化露头模型和定量储层地质知识库;结合多露头剖面精细解剖及对比研究... 详细信息

针对传统野外露头研究方法的不足,利用无人机倾斜摄影具有敏捷性、视角全局性及定量化程度高的特点,对野外三角洲露头数据进行了采集、处理与地质解释,建立了三维数字化露头模型和定量储层地质知识库;结合多露头剖面精细解剖及对比研究,采用岩相组合、层次结构、平剖互动、序贯指示随机模拟和多点地质统计模拟等方法,建立了露头区三维多点地质统计模型。研究结果表明:(1)无人机采集的倾斜摄影数据体在处理后的三维坐标体系与拍摄的高精度图像信息全面耦合,实现了野外露头地质信息三维数字化、可视化和定量化。在井点设置下,结合单井岩相约束,开展井间基准面旋回露头对比与小层追踪,建立了露头区空间地层框架和定量储层地质知识库。(2)将传统野外露头剖面解剖与现代无人机倾斜摄影定量储层地质知识库结合起来研究,是建立研究区地质属性空间分布和地质模式的互补手段,可以增强研究结论的合理性。露头区自下而上发育了构型差异较大的3类成因砂体,即交叉叠置型分流河道、分流河道与席状砂坝叠置型和薄层席状砂型或孤立分流河道型等骨架砂体类型。(3)无人机倾斜摄影模型将二维非规则露头剖面研究向三维空间拓展,建立了三维序贯指示随机模拟模型,不仅加强了剖面间的关联性,还为露头平面沉积微相研究、平剖互动精细刻画砂体构型分布提供了强大支撑。(4)基于平剖互动及定量储层地质知识库数据确定了三维训练图像结构模式,最后开展了三维多点地质统计模拟,并探讨了相控孔隙度参数及"甜点"分布规律。研究结果显示,多点地质统计模型的实现、各层砂体构型分布与定量储层地质知识库差异较小;同时,各种模型统计数据值差异较小,多点三维模拟实现的模型可靠程度高。

关键词：无人机倾斜摄影数字露头三维露头建模多点地质统计学训练图像三角洲储层构型坪头乡

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A statistical Edge Detection Framework for Noisy images 26

A Statistical Edge Detection Framework for Noisy Images

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26th IEEE Signal processing and Communications Applications conference (SIU)

作者： Duman, Elvan Erdem, O. Ayhan Gazi Univ Teknol Fak Bilgisayar Muhendisligi Bolumu Ankara Turkey

ISBN: (纸本)9781538615010

Noises can highly influence the performance of segmentation and edge detection process. Traditional edge detection methods are very vulnerable to noise. statistical models, which are based on t-test, Wilcoxon test, and rank-order test, are suggested for noisy images in the literature. In this paper, we suggest a framework based on rank-order test and k-means clustering, which increases the efficiency of the rank-order test. The performance of the proposed statistical framework was tested on corrupted images with different noise variance. Experimental results show that proposed edge detection framework is more robust to different noise variance than well-known conventional and statistical methods.

关键词： image processing edge detection statistical test rank-order test

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